Normalization strategy comparison for Data-driven analysis of isobarically labeled proteomic data.
Joris Van Houtven, Piotr Prostko
November 09, 2020,10:45
=======Choosing a Summarization strategy for Data-driven analysis of isobarically labeled proteomic data.
Joris Van Houtven, Piotr Prostko
November 06, 2020,15:32
>>>>>>> Stashed changeslibrary(ggplot2)
library(stringi)
library(gridExtra)
library(dendextend)
library(kableExtra)
library(psych)
library(limma)
library(tidyverse)
library(preprocessCore)
library(CONSTANd) # install from source: https://github.com/PDiracDelta/CONSTANd/
library(NOMAD) # devtools::install_github("carlmurie/NOMAD")
library(vsn)This notebook presents isobaric labeling data analysis strategy that includes data-driven normalization.
We will check how varying analysis components [summarization/normalization/differential abundance testing methods] changes end results of a quantitative proteomic study.
<<<<<<< Updated upstreamsource('other_functions.R')
source('plotting_functions.R')
# you should either make a symbolic link in this directory
data.list <- readRDS('input_data.rds')
=======
source('other_functions.R')
source('plotting_functions.R')
# you should either make a symbolic link in this directory
data.list <- readRDS('input_data.rds')
>>>>>>> Stashed changes
dat.l <- data.list$dat.l # data in long format
# dat.w <- data.list$dat.w # data in wide format
if ('X' %in% colnames(dat.l)) { dat.l$X <- NULL }
# remove shared peptides
shared.peptides <- dat.l %>% filter(!shared.peptide)
# keep spectra with isolation interference <30 and no missing quantification channels
dat.l <- dat.l %>% filter(isoInterOk & noNAs)
# which proteins were spiked in?
spiked.proteins <- dat.l %>% distinct(Protein) %>% filter(stri_detect(Protein, fixed='ups')) %>% pull %>% as.character
# which peptides were identified in each MS run?
unique.pep=dat.l %>%
group_by(Run) %>%
distinct(Peptide) %>%
mutate(val=1)
unique.pep <- xtabs(val~Peptide+Run, data=unique.pep)
tmp <- apply(unique.pep, 1, function(x) all(x==1))
inner.peptides <- rownames(unique.pep)[tmp]
# specify # of varying component variants and their names
<<<<<<< Updated upstream
variant.names <- c('quantile', 'CONSTANd', 'NOMAD', 'medianSweeping', 'CycLoessVSN')
n.comp.variants <- length(variant.names)
scale.vec <- c('log', 'log', 'log', 'log', 'log')
=======
variant.names <- c('quantile', 'CONSTANd', 'NOMAD', 'medianSweeping', 'CycLoessVSN')
n.comp.variants <- length(variant.names)
scale.vec <- c('log', 'log', 'log', 'log', 'log')
>>>>>>> Stashed changes
# pick reference channel and condition for making plots / doing DEA
referenceChannel <- '127C'
referenceCondition <- '0.5'
# specify colours corresponding to biological conditions
condition.colour <- tribble(
~Condition, ~Colour,
"0.125", 'black',
"0.5", 'blue',
"0.667", 'green',
"1", 'red' )
# create data frame with sample info (distinct Run,Channel, Sample, Condition, Colour)
sample.info <- get_sample_info(dat.l, condition.colour)
channelNames <- remove_factors(unique(sample.info$Channel))
<<<<<<< Updated upstream
1 Unit component: log2 transformation of reporter ion intensities
=======
1 Unit component
1.1 log2 transformation of reporter ion intensities
>>>>>>> Stashed changes
dat.unit.l <- dat.l %>% mutate(response=log2(Intensity)) %>% select(-Intensity)
2 Normalization component
# switch to wide format
dat.unit.w <- pivot_wider(data = dat.unit.l, id_cols=-one_of(c('Condition', 'BioReplicate')), names_from=Channel, values_from=response)
dat.norm.w <- emptyList(variant.names)
<<<<<<< Updated upstream
2.1 Quantile (1)
We apply quantile normalization to each Run separately, and then re-scale the observations so that the mean observation within in each run is set equal to the mean observation across all runs.
grand_average <- mean(as.matrix(dat.unit.w[,channelNames]))
x.split <- split(dat.unit.w, dat.unit.w$Run)
x.split.norm <- lapply(x.split, function(y) {
# apply normalizeQuantiles to each Run separately
y[,channelNames] <- normalize.quantiles(as.matrix(y[,channelNames]))
# make averages of all runs equal.
y[,channelNames] <- y[,channelNames] / mean(colMeans(y[,channelNames])) * grand_average
=======
2.1 Quantile
We apply quantile normalization to each Run separately, and then re-scale the observations so that the mean observation within in each run is set equal to the mean observation across all runs.
grand_average <- mean(as.matrix(dat.unit.w[,quanCols]))
x.split <- split(dat.unit.w, dat.unit.w$Run)
x.split.norm <- lapply(x.split, function(y) {
# apply normalizeQuantiles to each Run separately
y[,quanCols] <- normalize.quantiles(as.matrix(y[,quanCols]))
# make averages of all runs equal.
y[,quanCols] <- y[,quanCols] / mean(colMeans(y[,quanCols])) * grand_average
>>>>>>> Stashed changes
return(y)
})
dat.norm.w$quantile <- bind_rows(x.split.norm)
2.2 CONSTANd
<<<<<<< Updated upstream
# dat.unit.l entries are in long format so all have same colnames and no channelNames
=======
# dat.unit.l entries are in long format so all have same colnames and no quanCols
>>>>>>> Stashed changes
x.split <- split(dat.unit.w, dat.unit.w$Run) # apply CONSTANd to each Run separately
x.split.norm <- lapply(x.split, function(y) {
y[,channelNames] <- CONSTANd(y[,channelNames])$normalized_data
return(y)
})
dat.norm.w$CONSTANd <- bind_rows(x.split.norm)
2.3 NOMAD
We apply NOMAD on the PSM level instead of the peptide level.
# doRobust=F: use means, like CONSTANd; doLog=F: values are already transformed.
dat.nomadnorm <- nomadNormalization(dat.unit.l$response, dat.unit.l %>% rename(iTRAQ=Channel) %>% as.data.frame, doRobust = FALSE, doiTRAQCorrection = FALSE, doLog = FALSE)
## Running normalization with 464224 number of data points
## Normalizing for factor: Peptide
## Normalizing for factor: Run
## Normalizing for factor: iTRAQ
## Normalizing for factor: Run Peptide
## Normalizing for factor: Run iTRAQ
dat.nomadnorm$x$response <- dat.nomadnorm$y
dat.norm.w$NOMAD <- pivot_wider(data = dat.nomadnorm$x, id_cols=-one_of(c('Condition', 'BioReplicate')), names_from=iTRAQ, values_from=response)
<<<<<<< Updated upstream
2.4 medianSweeping (1)
=======
2.4 medianSweeping
>>>>>>> Stashed changes
# subtract the spectrum median log2intensity from the observed log2intensities
dat.norm.w$medianSweeping <- dat.unit.w
dat.norm.w$medianSweeping[,channelNames] <- sweep(dat.norm.w$medianSweeping[,channelNames], 1, apply(dat.norm.w$medianSweeping[,channelNames], 1, median) )
2.5 Cyclic loess + VSN normalization (1)
x.split <- split(dat.unit.w, dat.unit.w$Run)
x.split.norm <- lapply(x.split, function(y) {
y[,channelNames] <- normalizeCyclicLoess(y[,channelNames], span=0.005, iterations=15)
return(y)
})
dat.norm.w$CycLoessVSN <- bind_rows(x.split.norm)
vsn.tmp <- vsn2(as.matrix(dat.norm.w$CycLoessVSN[,channelNames]), strata=dat.norm.w$CycLoessVSN$Run)
dat.norm.w$CycLoessVSN[,channelNames] <- vsn.tmp@hx
2.5 Cyclic loess normalization
x.split <- split(dat.unit.w, dat.unit.w$Run)
x.split.norm <- lapply(x.split, function(y) {
y[,quanCols] <- normalizeCyclicLoess(y[,quanCols], span=0.005, iterations=15)
return(y)
})
dat.norm.w$CycLoessVSN <- bind_rows(x.split.norm)
vsn.tmp <- vsn2(as.matrix(dat.norm.w$CycLoessVSN[,quanCols]), strata=dat.norm.w$CycLoessVSN$Run)
dat.norm.w$CycLoessVSN[,quanCols] <- vsn.tmp@hx
3 Summarization component
Summarize quantification values from PSM to peptide (first step) to protein (second step).
3.1 Median summarization (PSM to peptide to protein)
# normalized data
dat.norm.summ.w <- lapply(dat.norm.w, function(x) {
# group by (run,)protein,peptide then summarize twice (once on each level)
# add select() statement because summarise_at is going bananas over character columns
y <- x %>% group_by(Run, Protein, Peptide) %>% select(Run, Protein, Peptide, channelNames) %>% summarise_at(.vars = channelNames, .funs = median) %>% select(Run, Protein, channelNames) %>% summarise_at(.vars = channelNames, .funs = median) %>% ungroup()
return(y)
})
<<<<<<< Updated upstream
Let’s also summarize the non-normalized data for in the comparison section.
=======
# make data completely wide (also across runs)
## normalized data
dat.norm.summ.w2 <- lapply(dat.norm.summ.w, function(x) {
return(x %>% pivot_wider(names_from = Run, values_from = all_of(channelNames), names_glue = "{Run}:{.value}"))
})
colnames(dat.norm.summ.w2)
## NULL
# apply normalizeQuantiles again, now to the data from all runs simultaneously
dat.norm.summ.w2$quantile[,sample.info$Sample] <- normalize.quantiles(as.matrix(dat.norm.summ.w2$quantile[,sample.info$Sample]))
dat.norm.summ.w2
## $quantile
## # A tibble: 4,083 x 33
## Protein `Mixture1_1:127~ `Mixture1_2:127~ `Mixture2_1:127~ `Mixture2_2:127~
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 A0AVT1 14.2 14.7 15.0 14.0
## 2 A0FGR8 14.2 14.8 15.2 14.4
## 3 A0MZ66 14.9 14.7 13.9 14.5
## 4 A1L0T0 14.3 13.6 NA 14.2
## 5 A3KMH1 11.0 11.1 10.5 NA
## 6 A4D1E9 15.2 15.0 NA 14.4
## 7 A5YKK6 14.6 NA NA 14.9
## 8 A6NDG6 10.3 12.2 10.4 15.5
## 9 A6NDU8 13.5 13.8 13.4 13.0
## 10 A6NHQ2 16.6 14.7 NA NA
## # ... with 4,073 more rows, and 28 more variables: `Mixture1_1:127N` <dbl>,
## # `Mixture1_2:127N` <dbl>, `Mixture2_1:127N` <dbl>, `Mixture2_2:127N` <dbl>,
## # `Mixture1_1:128C` <dbl>, `Mixture1_2:128C` <dbl>, `Mixture2_1:128C` <dbl>,
## # `Mixture2_2:128C` <dbl>, `Mixture1_1:128N` <dbl>, `Mixture1_2:128N` <dbl>,
## # `Mixture2_1:128N` <dbl>, `Mixture2_2:128N` <dbl>, `Mixture1_1:129C` <dbl>,
## # `Mixture1_2:129C` <dbl>, `Mixture2_1:129C` <dbl>, `Mixture2_2:129C` <dbl>,
## # `Mixture1_1:129N` <dbl>, `Mixture1_2:129N` <dbl>, `Mixture2_1:129N` <dbl>,
## # `Mixture2_2:129N` <dbl>, `Mixture1_1:130C` <dbl>, `Mixture1_2:130C` <dbl>,
## # `Mixture2_1:130C` <dbl>, `Mixture2_2:130C` <dbl>, `Mixture1_1:130N` <dbl>,
## # `Mixture1_2:130N` <dbl>, `Mixture2_1:130N` <dbl>, `Mixture2_2:130N` <dbl>
##
## $CONSTANd
## # A tibble: 4,083 x 33
## Protein `Mixture1_1:127~ `Mixture1_2:127~ `Mixture2_1:127~ `Mixture2_2:127~
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 A0AVT1 0.999 0.991 1.00 1.00
## 2 A0FGR8 0.993 1.00 1.00 0.999
## 3 A0MZ66 0.997 1.01 1.00 0.997
## 4 A1L0T0 1.01 1.00 NA 1.01
## 5 A3KMH1 1.05 1.03 0.999 NA
## 6 A4D1E9 1.01 0.998 NA 1.00
## 7 A5YKK6 0.995 NA NA 0.991
## 8 A6NDG6 0.999 0.993 0.995 0.998
## 9 A6NDU8 1.00 0.997 1.01 1.01
## 10 A6NHQ2 0.995 0.982 NA NA
## # ... with 4,073 more rows, and 28 more variables: `Mixture1_1:127N` <dbl>,
## # `Mixture1_2:127N` <dbl>, `Mixture2_1:127N` <dbl>, `Mixture2_2:127N` <dbl>,
## # `Mixture1_1:128C` <dbl>, `Mixture1_2:128C` <dbl>, `Mixture2_1:128C` <dbl>,
## # `Mixture2_2:128C` <dbl>, `Mixture1_1:128N` <dbl>, `Mixture1_2:128N` <dbl>,
## # `Mixture2_1:128N` <dbl>, `Mixture2_2:128N` <dbl>, `Mixture1_1:129C` <dbl>,
## # `Mixture1_2:129C` <dbl>, `Mixture2_1:129C` <dbl>, `Mixture2_2:129C` <dbl>,
## # `Mixture1_1:129N` <dbl>, `Mixture1_2:129N` <dbl>, `Mixture2_1:129N` <dbl>,
## # `Mixture2_2:129N` <dbl>, `Mixture1_1:130C` <dbl>, `Mixture1_2:130C` <dbl>,
## # `Mixture2_1:130C` <dbl>, `Mixture2_2:130C` <dbl>, `Mixture1_1:130N` <dbl>,
## # `Mixture1_2:130N` <dbl>, `Mixture2_1:130N` <dbl>, `Mixture2_2:130N` <dbl>
##
## $NOMAD
## # A tibble: 4,083 x 33
## Protein `Mixture1_1:127~ `Mixture1_2:127~ `Mixture2_1:127~ `Mixture2_2:127~
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 A0AVT1 -0.00791 -0.125 0.00800 0.0331
## 2 A0FGR8 -0.104 0.0339 -0.00415 -0.0203
## 3 A0MZ66 -0.0427 0.120 0.00582 -0.0431
## 4 A1L0T0 0.0953 0.0311 NA 0.152
## 5 A3KMH1 0.549 0.335 0.00572 NA
## 6 A4D1E9 0.187 -0.0381 NA 0.0706
## 7 A5YKK6 -0.0721 NA NA -0.145
## 8 A6NDG6 -0.00381 -0.0785 -0.0302 -0.0421
## 9 A6NDU8 0.0250 -0.0416 0.166 0.136
## 10 A6NHQ2 -0.0905 -0.279 NA NA
## # ... with 4,073 more rows, and 28 more variables: `Mixture1_1:127N` <dbl>,
## # `Mixture1_2:127N` <dbl>, `Mixture2_1:127N` <dbl>, `Mixture2_2:127N` <dbl>,
## # `Mixture1_1:128C` <dbl>, `Mixture1_2:128C` <dbl>, `Mixture2_1:128C` <dbl>,
## # `Mixture2_2:128C` <dbl>, `Mixture1_1:128N` <dbl>, `Mixture1_2:128N` <dbl>,
## # `Mixture2_1:128N` <dbl>, `Mixture2_2:128N` <dbl>, `Mixture1_1:129C` <dbl>,
## # `Mixture1_2:129C` <dbl>, `Mixture2_1:129C` <dbl>, `Mixture2_2:129C` <dbl>,
## # `Mixture1_1:129N` <dbl>, `Mixture1_2:129N` <dbl>, `Mixture2_1:129N` <dbl>,
## # `Mixture2_2:129N` <dbl>, `Mixture1_1:130C` <dbl>, `Mixture1_2:130C` <dbl>,
## # `Mixture2_1:130C` <dbl>, `Mixture2_2:130C` <dbl>, `Mixture1_1:130N` <dbl>,
## # `Mixture1_2:130N` <dbl>, `Mixture2_1:130N` <dbl>, `Mixture2_2:130N` <dbl>
##
## $medianSweeping
## # A tibble: 4,083 x 33
## Protein `Mixture1_1:127~ `Mixture1_2:127~ `Mixture2_1:127~ `Mixture2_2:127~
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 A0AVT1 -0.0215 -0.140 -0.0269 0.00507
## 2 A0FGR8 -0.130 0.0122 -0.0618 -0.0824
## 3 A0MZ66 -0.0429 0.0990 -0.0530 -0.102
## 4 A1L0T0 0.0111 0.00937 NA 0.0561
## 5 A3KMH1 0.563 0.216 -0.0436 NA
## 6 A4D1E9 0.169 -0.0629 NA 0.00864
## 7 A5YKK6 -0.0737 NA NA -0.239
## 8 A6NDG6 -0.0733 -0.0701 -0.129 -0.0980
## 9 A6NDU8 -0.00860 -0.105 0.0997 0.0165
## 10 A6NHQ2 -0.120 -0.208 NA NA
## # ... with 4,073 more rows, and 28 more variables: `Mixture1_1:127N` <dbl>,
## # `Mixture1_2:127N` <dbl>, `Mixture2_1:127N` <dbl>, `Mixture2_2:127N` <dbl>,
## # `Mixture1_1:128C` <dbl>, `Mixture1_2:128C` <dbl>, `Mixture2_1:128C` <dbl>,
## # `Mixture2_2:128C` <dbl>, `Mixture1_1:128N` <dbl>, `Mixture1_2:128N` <dbl>,
## # `Mixture2_1:128N` <dbl>, `Mixture2_2:128N` <dbl>, `Mixture1_1:129C` <dbl>,
## # `Mixture1_2:129C` <dbl>, `Mixture2_1:129C` <dbl>, `Mixture2_2:129C` <dbl>,
## # `Mixture1_1:129N` <dbl>, `Mixture1_2:129N` <dbl>, `Mixture2_1:129N` <dbl>,
## # `Mixture2_2:129N` <dbl>, `Mixture1_1:130C` <dbl>, `Mixture1_2:130C` <dbl>,
## # `Mixture2_1:130C` <dbl>, `Mixture2_2:130C` <dbl>, `Mixture1_1:130N` <dbl>,
## # `Mixture1_2:130N` <dbl>, `Mixture2_1:130N` <dbl>, `Mixture2_2:130N` <dbl>
##
## $CycLoessVSN
## # A tibble: 4,083 x 33
## Protein `Mixture1_1:127~ `Mixture1_2:127~ `Mixture2_1:127~ `Mixture2_2:127~
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 A0AVT1 12.5 12.5 12.5 12.5
## 2 A0FGR8 12.5 12.5 12.5 12.5
## 3 A0MZ66 12.5 12.5 12.5 12.5
## 4 A1L0T0 12.5 12.5 NA 12.5
## 5 A3KMH1 12.5 12.5 12.5 NA
## 6 A4D1E9 12.5 12.5 NA 12.5
## 7 A5YKK6 12.5 NA NA 12.5
## 8 A6NDG6 12.5 12.5 12.5 12.5
## 9 A6NDU8 12.5 12.5 12.5 12.5
## 10 A6NHQ2 12.5 12.5 NA NA
## # ... with 4,073 more rows, and 28 more variables: `Mixture1_1:127N` <dbl>,
## # `Mixture1_2:127N` <dbl>, `Mixture2_1:127N` <dbl>, `Mixture2_2:127N` <dbl>,
## # `Mixture1_1:128C` <dbl>, `Mixture1_2:128C` <dbl>, `Mixture2_1:128C` <dbl>,
## # `Mixture2_2:128C` <dbl>, `Mixture1_1:128N` <dbl>, `Mixture1_2:128N` <dbl>,
## # `Mixture2_1:128N` <dbl>, `Mixture2_2:128N` <dbl>, `Mixture1_1:129C` <dbl>,
## # `Mixture1_2:129C` <dbl>, `Mixture2_1:129C` <dbl>, `Mixture2_2:129C` <dbl>,
## # `Mixture1_1:129N` <dbl>, `Mixture1_2:129N` <dbl>, `Mixture2_1:129N` <dbl>,
## # `Mixture2_2:129N` <dbl>, `Mixture1_1:130C` <dbl>, `Mixture1_2:130C` <dbl>,
## # `Mixture2_1:130C` <dbl>, `Mixture2_2:130C` <dbl>, `Mixture1_1:130N` <dbl>,
## # `Mixture1_2:130N` <dbl>, `Mixture2_1:130N` <dbl>, `Mixture2_2:130N` <dbl>
Notice that the row sums are not equal to Ncols anymore, because the median summarization does not preserve them (but mean summarization does).
Let’s also summarize the non-normalized data for comparison in the next section.
>>>>>>> Stashed changes
# non-normalized data
# add select() statement because summarise_at is going bananas over character columns
dat.nonnorm.summ.w <- dat.unit.w %>% group_by(Run, Protein, Peptide) %>% select(Run, Protein, Peptide, channelNames) %>% summarise_at(.vars = channelNames, .funs = median) %>% select(Run, Protein, channelNames) %>% summarise_at(.vars = channelNames, .funs = median) %>% ungroup()
# make data completely wide (also across runs)
## normalized data
dat.norm.summ.w2 <- lapply(dat.norm.summ.w, function(x) {
return(x %>% pivot_wider(names_from = Run, values_from = all_of(channelNames), names_glue = "{Run}:{.value}"))
})
colnames(dat.norm.summ.w2)
## NULL
4 Normalization component (2)
4.1 Quantile (2)
# apply normalizeQuantiles again, now to the data from all runs simultaneously
dat.norm.summ.w2$quantile[,sample.info$Sample] <- normalize.quantiles(as.matrix(dat.norm.summ.w2$quantile[,sample.info$Sample]))
dat.norm.summ.w2
## $quantile
## # A tibble: 4,083 x 33
## Protein `Mixture1_1:127… `Mixture1_2:127… `Mixture2_1:127… `Mixture2_2:127…
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 A0AVT1 14.2 14.7 15.0 14.0
## 2 A0FGR8 14.2 14.8 15.2 14.4
## 3 A0MZ66 14.9 14.7 13.9 14.5
## 4 A1L0T0 14.3 13.6 NA 14.2
## 5 A3KMH1 11.0 11.1 10.5 NA
## 6 A4D1E9 15.2 15.0 NA 14.4
## 7 A5YKK6 14.6 NA NA 14.9
## 8 A6NDG6 10.3 12.2 10.4 15.5
## 9 A6NDU8 13.5 13.8 13.4 13.0
## 10 A6NHQ2 16.6 14.7 NA NA
## # … with 4,073 more rows, and 28 more variables: `Mixture1_1:127N` <dbl>,
## # `Mixture1_2:127N` <dbl>, `Mixture2_1:127N` <dbl>, `Mixture2_2:127N` <dbl>,
## # `Mixture1_1:128C` <dbl>, `Mixture1_2:128C` <dbl>, `Mixture2_1:128C` <dbl>,
## # `Mixture2_2:128C` <dbl>, `Mixture1_1:128N` <dbl>, `Mixture1_2:128N` <dbl>,
## # `Mixture2_1:128N` <dbl>, `Mixture2_2:128N` <dbl>, `Mixture1_1:129C` <dbl>,
## # `Mixture1_2:129C` <dbl>, `Mixture2_1:129C` <dbl>, `Mixture2_2:129C` <dbl>,
## # `Mixture1_1:129N` <dbl>, `Mixture1_2:129N` <dbl>, `Mixture2_1:129N` <dbl>,
## # `Mixture2_2:129N` <dbl>, `Mixture1_1:130C` <dbl>, `Mixture1_2:130C` <dbl>,
## # `Mixture2_1:130C` <dbl>, `Mixture2_2:130C` <dbl>, `Mixture1_1:130N` <dbl>,
## # `Mixture1_2:130N` <dbl>, `Mixture2_1:130N` <dbl>, `Mixture2_2:130N` <dbl>
##
## $CONSTANd
## # A tibble: 4,083 x 33
## Protein `Mixture1_1:127… `Mixture1_2:127… `Mixture2_1:127… `Mixture2_2:127…
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 A0AVT1 0.999 0.991 1.00 1.00
## 2 A0FGR8 0.993 1.00 1.00 0.999
## 3 A0MZ66 0.997 1.01 1.00 0.997
## 4 A1L0T0 1.01 1.00 NA 1.01
## 5 A3KMH1 1.05 1.03 0.999 NA
## 6 A4D1E9 1.01 0.998 NA 1.00
## 7 A5YKK6 0.995 NA NA 0.991
## 8 A6NDG6 0.999 0.993 0.995 0.998
## 9 A6NDU8 1.00 0.997 1.01 1.01
## 10 A6NHQ2 0.995 0.982 NA NA
## # … with 4,073 more rows, and 28 more variables: `Mixture1_1:127N` <dbl>,
## # `Mixture1_2:127N` <dbl>, `Mixture2_1:127N` <dbl>, `Mixture2_2:127N` <dbl>,
## # `Mixture1_1:128C` <dbl>, `Mixture1_2:128C` <dbl>, `Mixture2_1:128C` <dbl>,
## # `Mixture2_2:128C` <dbl>, `Mixture1_1:128N` <dbl>, `Mixture1_2:128N` <dbl>,
## # `Mixture2_1:128N` <dbl>, `Mixture2_2:128N` <dbl>, `Mixture1_1:129C` <dbl>,
## # `Mixture1_2:129C` <dbl>, `Mixture2_1:129C` <dbl>, `Mixture2_2:129C` <dbl>,
## # `Mixture1_1:129N` <dbl>, `Mixture1_2:129N` <dbl>, `Mixture2_1:129N` <dbl>,
## # `Mixture2_2:129N` <dbl>, `Mixture1_1:130C` <dbl>, `Mixture1_2:130C` <dbl>,
## # `Mixture2_1:130C` <dbl>, `Mixture2_2:130C` <dbl>, `Mixture1_1:130N` <dbl>,
## # `Mixture1_2:130N` <dbl>, `Mixture2_1:130N` <dbl>, `Mixture2_2:130N` <dbl>
##
## $NOMAD
## # A tibble: 4,083 x 33
## Protein `Mixture1_1:127… `Mixture1_2:127… `Mixture2_1:127… `Mixture2_2:127…
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 A0AVT1 -0.00791 -0.125 0.00800 0.0331
## 2 A0FGR8 -0.104 0.0339 -0.00415 -0.0203
## 3 A0MZ66 -0.0427 0.120 0.00582 -0.0431
## 4 A1L0T0 0.0953 0.0311 NA 0.152
## 5 A3KMH1 0.549 0.335 0.00572 NA
## 6 A4D1E9 0.187 -0.0381 NA 0.0706
## 7 A5YKK6 -0.0721 NA NA -0.145
## 8 A6NDG6 -0.00381 -0.0785 -0.0302 -0.0421
## 9 A6NDU8 0.0250 -0.0416 0.166 0.136
## 10 A6NHQ2 -0.0905 -0.279 NA NA
## # … with 4,073 more rows, and 28 more variables: `Mixture1_1:127N` <dbl>,
## # `Mixture1_2:127N` <dbl>, `Mixture2_1:127N` <dbl>, `Mixture2_2:127N` <dbl>,
## # `Mixture1_1:128C` <dbl>, `Mixture1_2:128C` <dbl>, `Mixture2_1:128C` <dbl>,
## # `Mixture2_2:128C` <dbl>, `Mixture1_1:128N` <dbl>, `Mixture1_2:128N` <dbl>,
## # `Mixture2_1:128N` <dbl>, `Mixture2_2:128N` <dbl>, `Mixture1_1:129C` <dbl>,
## # `Mixture1_2:129C` <dbl>, `Mixture2_1:129C` <dbl>, `Mixture2_2:129C` <dbl>,
## # `Mixture1_1:129N` <dbl>, `Mixture1_2:129N` <dbl>, `Mixture2_1:129N` <dbl>,
## # `Mixture2_2:129N` <dbl>, `Mixture1_1:130C` <dbl>, `Mixture1_2:130C` <dbl>,
## # `Mixture2_1:130C` <dbl>, `Mixture2_2:130C` <dbl>, `Mixture1_1:130N` <dbl>,
## # `Mixture1_2:130N` <dbl>, `Mixture2_1:130N` <dbl>, `Mixture2_2:130N` <dbl>
##
## $medianSweeping
## # A tibble: 4,083 x 33
## Protein `Mixture1_1:127… `Mixture1_2:127… `Mixture2_1:127… `Mixture2_2:127…
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 A0AVT1 -0.0215 -0.140 -0.0269 0.00507
## 2 A0FGR8 -0.130 0.0122 -0.0618 -0.0824
## 3 A0MZ66 -0.0429 0.0990 -0.0530 -0.102
## 4 A1L0T0 0.0111 0.00937 NA 0.0561
## 5 A3KMH1 0.563 0.216 -0.0436 NA
## 6 A4D1E9 0.169 -0.0629 NA 0.00864
## 7 A5YKK6 -0.0737 NA NA -0.239
## 8 A6NDG6 -0.0733 -0.0701 -0.129 -0.0980
## 9 A6NDU8 -0.00860 -0.105 0.0997 0.0165
## 10 A6NHQ2 -0.120 -0.208 NA NA
## # … with 4,073 more rows, and 28 more variables: `Mixture1_1:127N` <dbl>,
## # `Mixture1_2:127N` <dbl>, `Mixture2_1:127N` <dbl>, `Mixture2_2:127N` <dbl>,
## # `Mixture1_1:128C` <dbl>, `Mixture1_2:128C` <dbl>, `Mixture2_1:128C` <dbl>,
## # `Mixture2_2:128C` <dbl>, `Mixture1_1:128N` <dbl>, `Mixture1_2:128N` <dbl>,
## # `Mixture2_1:128N` <dbl>, `Mixture2_2:128N` <dbl>, `Mixture1_1:129C` <dbl>,
## # `Mixture1_2:129C` <dbl>, `Mixture2_1:129C` <dbl>, `Mixture2_2:129C` <dbl>,
## # `Mixture1_1:129N` <dbl>, `Mixture1_2:129N` <dbl>, `Mixture2_1:129N` <dbl>,
## # `Mixture2_2:129N` <dbl>, `Mixture1_1:130C` <dbl>, `Mixture1_2:130C` <dbl>,
## # `Mixture2_1:130C` <dbl>, `Mixture2_2:130C` <dbl>, `Mixture1_1:130N` <dbl>,
## # `Mixture1_2:130N` <dbl>, `Mixture2_1:130N` <dbl>, `Mixture2_2:130N` <dbl>
##
## $CycLoessVSN
## # A tibble: 4,083 x 33
## Protein `Mixture1_1:127… `Mixture1_2:127… `Mixture2_1:127… `Mixture2_2:127…
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 A0AVT1 9.92 9.81 9.90 10.1
## 2 A0FGR8 9.92 9.81 9.90 10.1
## 3 A0MZ66 9.92 9.81 9.90 10.1
## 4 A1L0T0 9.92 9.81 NA 10.1
## 5 A3KMH1 9.92 9.81 9.89 NA
## 6 A4D1E9 9.92 9.81 NA 10.1
## 7 A5YKK6 9.92 NA NA 10.1
## 8 A6NDG6 9.92 9.81 9.89 10.1
## 9 A6NDU8 9.92 9.81 9.90 10.1
## 10 A6NHQ2 9.93 9.81 NA NA
## # … with 4,073 more rows, and 28 more variables: `Mixture1_1:127N` <dbl>,
## # `Mixture1_2:127N` <dbl>, `Mixture2_1:127N` <dbl>, `Mixture2_2:127N` <dbl>,
## # `Mixture1_1:128C` <dbl>, `Mixture1_2:128C` <dbl>, `Mixture2_1:128C` <dbl>,
## # `Mixture2_2:128C` <dbl>, `Mixture1_1:128N` <dbl>, `Mixture1_2:128N` <dbl>,
## # `Mixture2_1:128N` <dbl>, `Mixture2_2:128N` <dbl>, `Mixture1_1:129C` <dbl>,
## # `Mixture1_2:129C` <dbl>, `Mixture2_1:129C` <dbl>, `Mixture2_2:129C` <dbl>,
## # `Mixture1_1:129N` <dbl>, `Mixture1_2:129N` <dbl>, `Mixture2_1:129N` <dbl>,
## # `Mixture2_2:129N` <dbl>, `Mixture1_1:130C` <dbl>, `Mixture1_2:130C` <dbl>,
## # `Mixture2_1:130C` <dbl>, `Mixture2_2:130C` <dbl>, `Mixture1_1:130N` <dbl>,
## # `Mixture1_2:130N` <dbl>, `Mixture2_1:130N` <dbl>, `Mixture2_2:130N` <dbl>
4.2 MedianSweeping (2)
# medianSweeping: in each channel, subtract median computed across all proteins within the channel, separately for each MS run.
x.split <- split(dat.norm.summ.w$medianSweeping, dat.norm.summ.w$medianSweeping$Run)
x.split.norm <- lapply(x.split, function(y) {
y[,channelNames] <- sweep(y[,channelNames], 2, apply(y[,channelNames], 2, median) )
return(y)
})
dat.norm.summ.w$medianSweeping <- bind_rows(x.split.norm)
4.3 VSN (2)
# additional VSN on protein level data applied to all runs
vsn.tmp <- vsn2(as.matrix(dat.norm.summ.w2$CycLoessVSN %>% select(-Protein)))
dat.norm.summ.w2$CycLoessVSN[, colnames(dat.norm.summ.w2$CycLoessVSN)!='Protein'] <- vsn.tmp@hx
5 QC plots
# make data completely wide (also across runs)
## non-normalized data
<<<<<<< Updated upstream
dat.nonnorm.summ.w2 <- dat.nonnorm.summ.w %>% pivot_wider(names_from = Run, values_from = all_of(channelNames), names_glue = "{Run}:{.value}")
=======
dat.nonnorm.summ.w2 <- dat.nonnorm.summ.w %>% pivot_wider(names_from = Run, values_from = all_of(quanCols), names_glue = "{Run}:{.value}")
>>>>>>> Stashed changes
## normalized data
# we already did this earlier, right before the second stage of quantile normalization!
dat.norm.summ.w2 <- lapply(dat.norm.summ.w, function(x) {
dat.tmp <- x %>% pivot_wider(names_from = Run, values_from = all_of(channelNames), names_sep = ":")
dat.tmp <- flip_colnames(dat.tmp, 'Protein')
return(dat.tmp)
})
# additional VSN on protein level data applied to all runs
vsn.tmp <- vsn2(as.matrix(dat.norm.summ.w2$CycLoessVSN %>% select(-Protein)))
dat.norm.summ.w2$CycLoessVSN[, colnames(dat.norm.summ.w2$CycLoessVSN)!='Protein'] <- vsn.tmp@hx
5.1 Boxplots
# use (half-)wide format
par(mfrow=c(3,2))
<<<<<<< Updated upstream
boxplot_w(dat.nonnorm.summ.w,sample.info, 'Raw')
for (i in 1: n.comp.variants){
boxplot_w(dat.norm.summ.w[[i]], sample.info, paste('Normalized', variant.names[i], sep='_'))
}
=======
boxplot_w(dat.nonnorm.summ.w,sample.info, 'Raw')
for (i in 1: n.comp.variants){
boxplot_w(dat.norm.summ.w[[i]], sample.info, paste('Normalized', variant.names[i], sep='_'))
}
>>>>>>> Stashed changes
par(mfrow=c(1, 1))
5.2 MA plots
MA plots of two single samples taken from condition 1 and condition 0.125, measured in different MS runs (samples Mixture2_1:127C and Mixture1_2:129N, respectively).
par(mfrow=c(3,2))
# different unit variants require different computation of fold changes and average abuandance: additive or multiplicative scale; see maplot_ils function
# use wide2 format
<<<<<<< Updated upstream
p <- vector('list', n.comp.variants+1)
p[[1]] <- maplot_ils(dat.nonnorm.summ.w2, 'Mixture2_1:127C', 'Mixture1_2:129N', scale.vec[i], 'Raw')
=======
p <- vector('list', n.comp.variants+1)
p[[1]] <- maplot_ils(dat.nonnorm.summ.w2, 'Mixture2_1:127C', 'Mixture1_2:129N', scale.vec[i], 'Raw')
>>>>>>> Stashed changes
for (i in 1: n.comp.variants){
p[[i+1]]<- maplot_ils(dat.norm.summ.w2[[i]], 'Mixture2_1:127C', 'Mixture1_2:129N', scale.vec[i], paste('Normalized', variant.names[i], sep='_'))
}
grid.arrange(grobs = p, ncol=2, nrow=3)
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
par(mfrow=c(1, 1))
MA plots of all samples from condition 1 and condition 0.125 (quantification values averaged within condition).
par(mfrow=c(3,2))
# different unit variants require different computation of fold changes and average abuandance: additive or multiplicative scale; see maplot_ils function
<<<<<<< Updated upstream
channels.num <- sample.info %>% filter(Condition=='1') %>% select(Sample) %>% pull
channels.denom <- sample.info %>% filter(Condition=='0.125') %>% select(Sample) %>% pull
=======
channels.num <- sample.info %>% filter(Condition=='1') %>% select(Sample) %>% pull
channels.denom <- sample.info %>% filter(Condition=='0.125') %>% select(Sample) %>% pull
>>>>>>> Stashed changes
p <- vector('list', n.comp.variants+1)
p[[1]] <- maplot_ils(dat.nonnorm.summ.w2, channels.num, channels.denom, scale.vec[i], 'Raw')
for (i in 1: n.comp.variants){
p[[i+1]]<- maplot_ils(dat.norm.summ.w2[[i]], channels.num, channels.denom, scale.vec[i], paste('Normalized', variant.names[i], sep='_'))
}
grid.arrange(grobs = p, ncol=2, nrow=3)
<<<<<<< Updated upstream
par(mfrow=c(1, 1))
5.3 CV (coefficient of variation) plots
par(mfrow=c(3,2))
dat.nonnorm.summ.l <- lapply(list(dat.nonnorm.summ.w), function(x){
x$Mixture <- unlist(lapply(stri_split(x$Run,fixed='_'), function(y) y[1]))
=======
par(mfrow=c(1, 1))
4.3 CV (coefficient of variation) plots
par(mfrow=c(3,2))
dat.nonnorm.summ.l <- lapply(list(dat.nonnorm.summ.w), function(x){
x$Mixture <- unlist(lapply(stri_split(x$Run,fixed='_'), function(y) y[1]))
>>>>>>> Stashed changes
x <- to_long_format(x, sample.info)
})
dat.norm.summ.l <- lapply(dat.norm.summ.w, function(x){
<<<<<<< Updated upstream
x$Mixture <- unlist(lapply(stri_split(x$Run,fixed='_'), function(y) y[1]))
=======
x$Mixture <- unlist(lapply(stri_split(x$Run,fixed='_'), function(y) y[1]))
>>>>>>> Stashed changes
x <- to_long_format(x, sample.info)
})
par(mfrow=c(2, 2))
cvplot_ils(dat=dat.nonnorm.summ.l[[1]], feature.group='Protein', xaxis.group='Condition',
title='Raw', abs=F)
for (i in 1: n.comp.variants){
cvplot_ils(dat=dat.norm.summ.l[[i]], feature.group='Protein', xaxis.group='Condition',
title=paste('Normalized', variant.names[i], sep='_'), abs=F)
}
<<<<<<< Updated upstream
par(mfrow=c(1, 1))
=======
par(mfrow=c(1, 1))
>>>>>>> Stashed changes
5.4 PCA plots
<<<<<<< Updated upstream
5.4.1 Using all proteins
par(mfrow=c(3,2))
pcaplot_ils(dat.nonnorm.summ.w2 %>% select(-'Protein'), info=sample.info, 'Raw')
=======
4.4.1 Using all proteins
par(mfrow=c(3,2))
pcaplot_ils(dat.nonnorm.summ.w2 %>% select(-'Protein'), info=sample.info, 'Raw')
>>>>>>> Stashed changes
for (i in seq_along(dat.norm.summ.w2)){
# select only the spiked.proteins
pcaplot_ils(dat.norm.summ.w2[[i]] %>% select(-'Protein'), info=sample.info, paste('Normalized', variant.names[i], sep='_'))
}
<<<<<<< Updated upstream
par(mfrow=c(1, 1))
5.4.2 Using spiked proteins only
par(mfrow=c(3,2))
pcaplot_ils(dat.nonnorm.summ.w2 %>% filter(Protein %in% spiked.proteins) %>% select(-'Protein'), info=sample.info, 'Raw')
=======
par(mfrow=c(1, 1))
4.4.2 Using spiked proteins only
par(mfrow=c(3,2))
pcaplot_ils(dat.nonnorm.summ.w2 %>% filter(Protein %in% spiked.proteins) %>% select(-'Protein'), info=sample.info, 'Raw')
>>>>>>> Stashed changes
for (i in seq_along(dat.norm.summ.w2)){
# select only the spiked.proteins
pcaplot_ils(dat.norm.summ.w2[[i]] %>% filter(Protein %in% spiked.proteins) %>% select(-'Protein'), info=sample.info, paste('Normalized', variant.names[i], sep='_'))
}
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
par(mfrow=c(1, 1))
5.5 HC (hierarchical clustering) plots
Only use spiked proteins
par(mfrow=c(3,2))
<<<<<<< Updated upstream
dendrogram_ils(dat.nonnorm.summ.w2 %>% filter(Protein %in% spiked.proteins) %>% select(-Protein), info=sample.info, 'Raw')
=======
dendrogram_ils(dat.nonnorm.summ.w2 %>% filter(Protein %in% spiked.proteins) %>% select(-Protein), info=sample.info, 'Raw')
>>>>>>> Stashed changes
for (i in seq_along(dat.norm.summ.w2)){
dendrogram_ils(dat.norm.summ.w2[[i]] %>% filter(Protein %in% spiked.proteins) %>% select(-Protein), info=sample.info, paste('Normalized', variant.names[i], sep='_'))
}
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
par(mfrow=c(1, 1))
6 DEA component: Moderated t-test
TODO: - Also try to log-transform the intensity case, to see if there are large differences in the t-test results. - done. remove this code? NOTE: - actually, lmFit (used in moderated_ttest) was built for log2-transformed data. However, supplying untransformed intensities can also work. This just means that the effects in the linear model are also additive on the untransformed scale, whereas for log-transformed data they are multiplicative on the untransformed scale. Also, there may be a bias which occurs from biased estimates of the population means in the t-tests, as mean(X) is not equal to exp(mean(log(X))).
design.matrix <- get_design_matrix(referenceCondition, sample.info)
dat.dea <- emptyList(names(dat.norm.summ.w2))
for (i in seq_along(dat.norm.summ.w2)) {
this_scale <- scale.vec[match(names(dat.dea)[i], variant.names)]
d <- column_to_rownames(as.data.frame(dat.norm.summ.w2[[i]]), 'Protein')
dat.dea[[i]] <- moderated_ttest(dat=d, design.matrix, scale=this_scale)
}
# also see what the unnormalized results would look like
n.comp.variants <- n.comp.variants + 1
variant.names <- c(variant.names, 'raw')
scale.vec <- c(scale.vec, 'raw')
dat.dea$raw <- moderated_ttest(dat=column_to_rownames(dat.nonnorm.summ.w2, 'Protein'), design.matrix, scale='raw')
7 Results comparison
7.1 Confusion matrix
cm <- conf_mat(dat.dea, 'q.mod', 0.05, spiked.proteins)
print_conf_mat(cm)
background
spiked
background
spiked
background
spiked
background
spiked
background
spiked
background
spiked
not_DE
4064
15
4061
5
4062
4
4061
4
4064
15
4064
15
4064
15
DE
0
4
3
14
2
15
3
15
<<<<<<< Updated upstream
0
=======
>>>>>>> Stashed changes
0
4
0
4
quantile
CONSTANd
NOMAD
medianSweeping
CycLoessVSN
raw
Accuracy
0.996
0.998
0.999
<<<<<<< Updated upstream
0.998
=======
>>>>>>> Stashed changes
0.998
0.996
0.996
0.996
Sensitivity
0.211
0.737
0.789
<<<<<<< Updated upstream
0.789
=======
>>>>>>> Stashed changes
0.789
0.211
0.211
0.211
Specificity
1.000
0.999
1.000
0.999
1.000
1.000
PPV
1.000
0.824
0.882
<<<<<<< Updated upstream
0.833
=======
>>>>>>> Stashed changes
0.833
1.000
1.000
1.000
NPV
0.996
0.999
<<<<<<< Updated upstream
=======
0.999
0.999
>>>>>>> Stashed changes
0.999
0.999
0.996
0.996
background
spiked
background
spiked
background
spiked
background
spiked
background
spiked
background
spiked
not_DE
4064
19
4064
15
4064
17
4062
17
4064
19
4064
19
DE
0
0
0
4
0
2
2
2
0
0
0
0
quantile
CONSTANd
NOMAD
medianSweeping
CycLoessVSN
raw
Accuracy
0.995
0.996
0.996
0.995
0.995
0.995
Sensitivity
0.000
0.211
0.105
0.105
0.000
0.000
Specificity
1.000
1.000
1.000
1.000
1.000
1.000
PPV
NaN
1.000
1.000
0.500
NaN
NaN
NPV
0.995
0.996
0.996
0.996
0.995
0.995
background
spiked
background
spiked
background
spiked
background
spiked
background
spiked
background
spiked
not_DE
4064
17
4059
5
4062
5
4060
5
4064
<<<<<<< Updated upstream
19
=======
15
>>>>>>> Stashed changes
4064
17
DE
0
2
5
14
2
14
4
14
0
<<<<<<< Updated upstream
0
=======
4
>>>>>>> Stashed changes
0
2
quantile
CONSTANd
NOMAD
medianSweeping
CycLoessVSN
raw
Accuracy
0.996
0.998
0.998
0.998
0.995
0.996
0.996
Sensitivity
0.105
0.737
0.737
0.737
<<<<<<< Updated upstream
0.000
=======
0.211
>>>>>>> Stashed changes
0.105
Specificity
1.000
0.999
1.000
0.999
1.000
1.000
PPV
1.000
0.737
0.875
0.778
NaN
1.000
1.000
NPV
0.996
0.999
0.999
0.999
0.995
0.996
0.996
7.2 Scatter plots
TO DO: Piotr: constant NOMAD i RAW q-values (approx. 1) generate error in scatterplots
# character vectors containing logFC and p-values columns
dea.cols <- colnames(dat.dea[[1]])
logFC.cols <- dea.cols[stri_detect_fixed(dea.cols, 'logFC')]
q.cols <- dea.cols[stri_detect_fixed(dea.cols, 'q.mod')]
n.contrasts <- length(logFC.cols)
<<<<<<< Updated upstream
#scatterplot_ils(dat.dea, q.cols, 'p-values') # commented due to error, sd=0 for NOMAD and RAW
scatterplot_ils(dat.dea, logFC.cols, 'log2FC')
=======
#scatterplot_ils(dat.dea, q.cols, 'p-values') # commented due to error, sd=0 for NOMAD and RAW
scatterplot_ils(dat.dea, logFC.cols, 'log2FC')
>>>>>>> Stashed changes
7.3 Volcano plots
for (i in 1:n.contrasts){
volcanoplot_ils(dat.dea, i, spiked.proteins)
}
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
7.4 Violin plots
Let’s see whether the spiked protein fold changes make sense
# plot theoretical value (horizontal lines) and violin per condition
dat.spiked.logfc <- lapply(dat.dea, function(x) x[spiked.proteins,logFC.cols])
dat.spiked.logfc.l <- lapply(dat.spiked.logfc, function(x) {
x %>% rename_with(function(y) sapply(y, function(z) strsplit(z, '_')[[1]][2])) %>% pivot_longer(cols = everything(), names_to = 'condition', values_to = 'logFC') %>% add_column(Protein=rep(rownames(x), each=length(colnames(x)))) })
violinplot_ils(lapply(dat.spiked.logfc.l, filter, condition != referenceCondition))
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
8 Conclusions
9 Session information
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=de_BE.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=de_BE.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=de_BE.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=de_BE.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
<<<<<<< Updated upstream
## [1] vsn_3.57.0 Biobase_2.49.1 BiocGenerics_0.35.4
## [4] NOMAD_0.99.0 dplR_1.7.1 CONSTANd_0.99.4
## [7] preprocessCore_1.51.0 forcats_0.5.0 stringr_1.4.0
## [10] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
## [13] tidyr_1.1.2 tibble_3.0.4 tidyverse_1.3.0
## [16] limma_3.45.19 psych_2.0.9 kableExtra_1.3.1
=======
## [1] vsn_3.56.0 Biobase_2.48.0 BiocGenerics_0.34.0
## [4] NOMAD_0.99.0 dplR_1.7.1 CONSTANd_0.99.0
## [7] preprocessCore_1.50.0 forcats_0.5.0 stringr_1.4.0
## [10] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
## [13] tidyr_1.1.2 tibble_3.0.4 tidyverse_1.3.0
## [16] limma_3.44.3 psych_2.0.9 kableExtra_1.3.1
>>>>>>> Stashed changes
## [19] dendextend_1.14.0 gridExtra_2.3 stringi_1.5.3
## [22] ggplot2_3.3.2
##
## loaded via a namespace (and not attached):
<<<<<<< Updated upstream
## [1] colorspace_1.4-1 ellipsis_0.3.1 class_7.3-17
## [4] fs_1.5.0 rstudioapi_0.11 farver_2.0.3
## [7] affyio_1.59.0 prodlim_2019.11.13 fansi_0.4.1
## [10] lubridate_1.7.9 xml2_1.3.2 codetools_0.2-16
## [13] splines_4.0.3 R.methodsS3_1.8.1 mnormt_2.0.2
## [16] knitr_1.30 jsonlite_1.7.1 pROC_1.16.2
## [19] caret_6.0-86 broom_0.7.2 dbplyr_1.4.4
## [22] png_0.1-7 R.oo_1.24.0 BiocManager_1.30.10
## [25] compiler_4.0.3 httr_1.4.2 backports_1.1.10
## [28] assertthat_0.2.1 Matrix_1.2-18 cli_2.1.0
## [31] htmltools_0.5.0 tools_4.0.3 gtable_0.3.0
## [34] glue_1.4.2 affy_1.67.1 reshape2_1.4.4
## [37] Rcpp_1.0.5 cellranger_1.1.0 vctrs_0.3.4
## [40] nlme_3.1-150 iterators_1.0.13 timeDate_3043.102
## [43] xfun_0.18 gower_0.2.2 rvest_0.3.6
## [46] lifecycle_0.2.0 XML_3.99-0.5 zlibbioc_1.35.0
## [49] MASS_7.3-53 scales_1.1.1 ipred_0.9-9
## [52] hms_0.5.3 yaml_2.2.1 rpart_4.1-15
## [55] highr_0.8 foreach_1.5.1 e1071_1.7-4
## [58] lava_1.6.8 rlang_0.4.8 pkgconfig_2.0.3
## [61] matrixStats_0.57.0 evaluate_0.14 lattice_0.20-41
## [64] recipes_0.1.14 labeling_0.4.2 tidyselect_1.1.0
## [67] plyr_1.8.6 magrittr_1.5 R6_2.4.1
## [70] generics_0.0.2 DBI_1.1.0 pillar_1.4.6
## [73] haven_2.3.1 withr_2.3.0 mgcv_1.8-33
## [76] survival_3.2-7 nnet_7.3-14 modelr_0.1.8
## [79] crayon_1.3.4 utf8_1.1.4 tmvnsim_1.0-2
## [82] rmarkdown_2.5 viridis_0.5.1 grid_4.0.3
## [85] readxl_1.3.1 data.table_1.13.2 blob_1.2.1
## [88] ModelMetrics_1.2.2.2 reprex_0.3.0 digest_0.6.27
## [91] webshot_0.5.2 R.utils_2.10.1 signal_0.7-6
## [94] stats4_4.0.3 munsell_0.5.0 viridisLite_0.3.0
=======
## [1] nlme_3.1-149 matrixStats_0.57.0 fs_1.5.0
## [4] lubridate_1.7.9 webshot_0.5.2 httr_1.4.2
## [7] tools_4.0.3 backports_1.1.10 utf8_1.1.4
## [10] R6_2.5.0 affyio_1.58.0 rpart_4.1-15
## [13] DBI_1.1.0 mgcv_1.8-33 colorspace_1.4-1
## [16] nnet_7.3-14 withr_2.3.0 tidyselect_1.1.0
## [19] mnormt_2.0.2 compiler_4.0.3 cli_2.1.0
## [22] rvest_0.3.6 xml2_1.3.2 labeling_0.4.2
## [25] scales_1.1.1 affy_1.66.0 digest_0.6.26
## [28] rmarkdown_2.5 R.utils_2.10.1 pkgconfig_2.0.3
## [31] htmltools_0.5.0 highr_0.8 dbplyr_2.0.0
## [34] rlang_0.4.8 readxl_1.3.1 rstudioapi_0.11
## [37] farver_2.0.3 generics_0.1.0 jsonlite_1.7.1
## [40] ModelMetrics_1.2.2.2 R.oo_1.24.0 magrittr_1.5
## [43] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0
## [46] fansi_0.4.1 viridis_0.5.1 lifecycle_0.2.0
## [49] R.methodsS3_1.8.1 pROC_1.16.2 yaml_2.2.1
## [52] MASS_7.3-53 zlibbioc_1.34.0 recipes_0.1.14
## [55] plyr_1.8.6 grid_4.0.3 crayon_1.3.4
## [58] lattice_0.20-41 haven_2.3.1 splines_4.0.3
## [61] hms_0.5.3 tmvnsim_1.0-2 knitr_1.30
## [64] pillar_1.4.6 stats4_4.0.3 reshape2_1.4.4
## [67] codetools_0.2-16 reprex_0.3.0 XML_3.99-0.5
## [70] glue_1.4.2 evaluate_0.14 data.table_1.13.2
## [73] BiocManager_1.30.10 modelr_0.1.8 foreach_1.5.1
## [76] png_0.1-7 vctrs_0.3.4 cellranger_1.1.0
## [79] gtable_0.3.0 assertthat_0.2.1 gower_0.2.2
## [82] xfun_0.18 prodlim_2019.11.13 broom_0.7.2
## [85] e1071_1.7-4 survival_3.2-7 class_7.3-17
## [88] viridisLite_0.3.0 timeDate_3043.102 signal_0.7-6
## [91] iterators_1.0.13 lava_1.6.8 caret_6.0-86
## [94] ellipsis_0.3.1 ipred_0.9-9
>>>>>>> Stashed changes